Main network model diagnostics - balanced statistics
This file shows diagnostics for main network models fit using balanced racial/ethnic mixing matrices and degree terms adjusted to correspond to the balanced mixing matrices. In this file, we compare models with regional assortativity ranging from 60% to 100%.
Load packages and model fits
rm(list = ls())
suppressMessages(library("EpiModelHIV"))
library("latticeExtra")## Loading required package: lattice## Loading required package: RColorBrewerlibrary("knitr")
library("kableExtra")
library("here")## here() starts at /homes/dpwhite/R/GitHub Repos/WHAMPload(file = here("Model fits and simulations/Fit tests and debugging/est/fit.m.testregionmix.bal.rda"))Model terms and control settings
| Terms | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 |
|---|---|---|---|---|---|
| edges | 2240.5 | 2240.5 | 2240.5 | 2240.5 | 2240.5 |
| nodefactor.deg.pers.1 | 493.0 | 493.0 | 493.0 | 493.0 | 493.0 |
| nodefactor.deg.pers.2 | 603.0 | 603.0 | 603.0 | 603.0 | 603.0 |
| nodefactor.race..wa.B | 213.8 | 213.8 | 213.8 | 213.8 | 213.8 |
| nodefactor.race..wa.H | 587.8 | 587.8 | 587.8 | 587.8 | 587.8 |
| nodefactor.region.EW | 445.6 | 445.6 | 445.6 | 445.6 | 445.6 |
| nodefactor.region.OW | 1278.1 | 1278.1 | 1278.1 | 1278.1 | 1278.1 |
| nodematch.race..wa.B | 31.2 | 31.2 | 31.2 | 31.2 | 31.2 |
| nodematch.race..wa.H | 123.3 | 123.3 | 123.3 | 123.3 | 123.3 |
| nodematch.race..wa.O | 1638.9 | 1638.9 | 1638.9 | 1638.9 | 1638.9 |
| absdiff.sqrt.age | 1206.3 | 1206.3 | 1206.3 | 1206.3 | 1206.3 |
| nodematch.region | 1344.3 | 1568.3 | 1792.4 | 1792.4 | NA |
| degrange | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| nodematch.role.class.I | -Inf | -Inf | -Inf | -Inf | -Inf |
| nodematch.role.class.R | -Inf | -Inf | -Inf | -Inf | -Inf |
| mix.region.EW.KC | NA | NA | NA | NA | -Inf |
| mix.region.EW.OW | NA | NA | NA | NA | -Inf |
| mix.region.KC.OW | NA | NA | NA | NA | -Inf |
The control settings for these models are:
set.control.ergm = control.ergm(MCMC.interval = 1e+5,
MCMC.samplesize = 7500,
MCMC.burnin = 1e+6,
MPLE.max.dyad.types = 1e+7,
init.method = "zeros",
MCMLE.maxit = 400,
parallel = np/2,
parallel.type="PSOCK"))MCMC diagnostics
Model 1
## Sample statistics summary:
##
## Iterations = 1e+06:750900000
## Thinning interval = 1e+05
## Number of chains = 4
## Sample size per chain = 7500
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.85713 28.944 0.16711 0.17018
## nodefactor.deg.pers.1 0.42010 17.439 0.10068 0.10295
## nodefactor.deg.pers.2 0.17487 18.786 0.10846 0.11217
## nodefactor.race..wa.B -0.02193 12.628 0.07291 0.08028
## nodefactor.race..wa.H 0.01380 17.344 0.10013 0.12621
## nodefactor.region.EW 0.57737 16.266 0.09391 0.10200
## nodefactor.region.OW 1.81257 30.871 0.17823 0.18046
## nodematch.race..wa.B -0.11057 5.008 0.02891 0.03802
## nodematch.race..wa.H -0.45348 8.698 0.05022 0.08752
## nodematch.race..wa.O 0.35114 26.424 0.15256 0.15440
## absdiff.sqrt.age 2.52820 28.655 0.16544 0.16796
## nodematch.region -0.21397 27.862 0.16086 0.17646
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -55.500 -18.500 1.500e+00 20.500 57.500
## nodefactor.deg.pers.1 -33.000 -11.000 5.684e-14 12.000 35.000
## nodefactor.deg.pers.2 -36.000 -13.000 0.000e+00 13.000 37.000
## nodefactor.race..wa.B -24.834 -8.834 1.664e-01 8.166 25.166
## nodefactor.race..wa.H -33.844 -11.844 1.560e-01 12.156 34.156
## nodefactor.region.EW -31.561 -10.561 4.392e-01 11.439 32.439
## nodefactor.region.OW -59.131 -19.131 1.869e+00 22.869 62.869
## nodematch.race..wa.B -9.177 -3.177 -1.769e-01 2.823 9.823
## nodematch.race..wa.H -17.300 -6.300 -3.003e-01 5.700 16.700
## nodematch.race..wa.O -51.946 -17.946 5.397e-02 18.054 52.054
## absdiff.sqrt.age -53.548 -16.835 2.545e+00 21.781 59.381
## nodematch.region -54.300 -19.300 -3.000e-01 18.700 54.700
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.pers.1
## edges 1.0000000 0.39830117
## nodefactor.deg.pers.1 0.3983012 1.00000000
## nodefactor.deg.pers.2 0.4354670 0.03996947
## nodefactor.race..wa.B 0.2652041 0.09975084
## nodefactor.race..wa.H 0.3578473 0.16714747
## nodefactor.region.EW 0.3395280 0.15076294
## nodefactor.region.OW 0.6088513 0.21541084
## nodematch.race..wa.B 0.1069200 0.03456114
## nodematch.race..wa.H 0.1269955 0.06258194
## nodematch.race..wa.O 0.8199826 0.31870579
## absdiff.sqrt.age 0.5501817 0.21131535
## nodematch.region 0.6069642 0.24927520
## nodefactor.deg.pers.2 nodefactor.race..wa.B
## edges 0.43546696 0.26520415
## nodefactor.deg.pers.1 0.03996947 0.09975084
## nodefactor.deg.pers.2 1.00000000 0.14787993
## nodefactor.race..wa.B 0.14787993 1.00000000
## nodefactor.race..wa.H 0.13699480 0.04948244
## nodefactor.region.EW 0.12925871 0.04606679
## nodefactor.region.OW 0.27253231 0.13148401
## nodematch.race..wa.B 0.06876969 0.56871546
## nodematch.race..wa.H 0.04877886 -0.04401211
## nodematch.race..wa.O 0.35664415 -0.04782852
## absdiff.sqrt.age 0.23688649 0.14268824
## nodematch.region 0.26735305 0.17323382
## nodefactor.race..wa.H nodefactor.region.EW
## edges 0.35784729 0.339527963
## nodefactor.deg.pers.1 0.16714747 0.150762940
## nodefactor.deg.pers.2 0.13699480 0.129258710
## nodefactor.race..wa.B 0.04948244 0.046066787
## nodefactor.race..wa.H 1.00000000 0.240524513
## nodefactor.region.EW 0.24052451 1.000000000
## nodefactor.region.OW 0.19720908 0.047507823
## nodematch.race..wa.B -0.02661595 0.005238238
## nodematch.race..wa.H 0.58568770 0.119494710
## nodematch.race..wa.O -0.06556770 0.240607581
## absdiff.sqrt.age 0.19023529 0.182923170
## nodematch.region 0.19524344 0.017042825
## nodefactor.region.OW nodematch.race..wa.B
## edges 0.60885126 0.106920039
## nodefactor.deg.pers.1 0.21541084 0.034561142
## nodefactor.deg.pers.2 0.27253231 0.068769693
## nodefactor.race..wa.B 0.13148401 0.568715463
## nodefactor.race..wa.H 0.19720908 -0.026615954
## nodefactor.region.EW 0.04750782 0.005238238
## nodefactor.region.OW 1.00000000 0.049484392
## nodematch.race..wa.B 0.04948439 1.000000000
## nodematch.race..wa.H 0.07016869 0.009666522
## nodematch.race..wa.O 0.51785819 0.025263658
## absdiff.sqrt.age 0.34110006 0.057440260
## nodematch.region 0.27677829 0.076830078
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.126995537 0.81998261
## nodefactor.deg.pers.1 0.062581942 0.31870579
## nodefactor.deg.pers.2 0.048778856 0.35664415
## nodefactor.race..wa.B -0.044012106 -0.04782852
## nodefactor.race..wa.H 0.585687703 -0.06556770
## nodefactor.region.EW 0.119494710 0.24060758
## nodefactor.region.OW 0.070168692 0.51785819
## nodematch.race..wa.B 0.009666522 0.02526366
## nodematch.race..wa.H 1.000000000 0.07213181
## nodematch.race..wa.O 0.072131808 1.00000000
## absdiff.sqrt.age 0.066784845 0.45599221
## nodematch.region 0.068205038 0.50530819
## absdiff.sqrt.age nodematch.region
## edges 0.55018173 0.60696420
## nodefactor.deg.pers.1 0.21131535 0.24927520
## nodefactor.deg.pers.2 0.23688649 0.26735305
## nodefactor.race..wa.B 0.14268824 0.17323382
## nodefactor.race..wa.H 0.19023529 0.19524344
## nodefactor.region.EW 0.18292317 0.01704283
## nodefactor.region.OW 0.34110006 0.27677829
## nodematch.race..wa.B 0.05744026 0.07683008
## nodematch.race..wa.H 0.06678485 0.06820504
## nodematch.race..wa.O 0.45599221 0.50530819
## absdiff.sqrt.age 1.00000000 0.33124676
## nodematch.region 0.33124676 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.011986211 0.020638562 0.034368252
## Lag 2e+05 -0.007377647 0.022420563 0.006762768
## Lag 3e+05 0.018374602 0.001926563 0.012681587
## Lag 4e+05 0.002562049 -0.006619428 0.021483577
## Lag 5e+05 -0.004646742 0.016302901 -0.008052570
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.067803688 0.12295898 0.060835867
## Lag 2e+05 0.010371454 0.04755312 0.024719655
## Lag 3e+05 0.006698791 0.03520064 0.019490749
## Lag 4e+05 0.004306251 0.03108922 0.003043523
## Lag 5e+05 0.009523224 0.04011344 0.027700080
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 -0.005059324 0.22317192 0.39370606
## Lag 2e+05 -0.004725715 0.07979811 0.21514107
## Lag 3e+05 0.005553057 0.02475657 0.14204853
## Lag 4e+05 0.011502099 0.02949872 0.09643100
## Lag 5e+05 0.002887068 0.02475090 0.07441011
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.018818396 0.015893337 0.065430480
## Lag 2e+05 0.004366621 0.001212934 -0.003026137
## Lag 3e+05 0.011325789 0.004790617 0.010653063
## Lag 4e+05 -0.007664547 -0.013591787 0.008862817
## Lag 5e+05 -0.014137325 0.003428966 -0.010979142
## Chain 2
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0149144922 0.012767491 0.028385573
## Lag 2e+05 0.0139751190 -0.001713105 0.017760680
## Lag 3e+05 0.0060649979 0.012526683 0.002162351
## Lag 4e+05 0.0129170119 -0.004648431 0.005093341
## Lag 5e+05 -0.0009625772 -0.006973087 0.001890850
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.0000000000 1.00000000 1.00000000
## Lag 1e+05 0.0649305755 0.12651082 0.05440163
## Lag 2e+05 0.0064496174 0.07227622 0.03636365
## Lag 3e+05 -0.0007567213 0.03522148 0.02746633
## Lag 4e+05 0.0183212250 0.01424554 0.00722692
## Lag 5e+05 0.0082165327 0.01122357 0.02337856
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.00000000
## Lag 1e+05 0.013680327 0.2099529599 0.41395961
## Lag 2e+05 0.005422657 0.0684885526 0.23114807
## Lag 3e+05 0.015357448 0.0275754765 0.13110671
## Lag 4e+05 0.015746321 0.0155248133 0.08331707
## Lag 5e+05 0.006851843 0.0009759034 0.05551397
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.006296424 0.034336630 0.071230316
## Lag 2e+05 0.003585546 0.017359263 0.024849189
## Lag 3e+05 0.010418592 0.005629930 0.008968681
## Lag 4e+05 0.029551730 0.005481986 0.009717249
## Lag 5e+05 0.005371301 0.004427086 0.006400640
## Chain 3
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.0000000000 1.00000000
## Lag 1e+05 0.022157389 0.0179917942 0.02536860
## Lag 2e+05 0.011089042 0.0084134564 0.00739670
## Lag 3e+05 0.006785859 -0.0230380218 -0.00124068
## Lag 4e+05 0.016302008 0.0061926660 0.01769187
## Lag 5e+05 -0.007066064 -0.0001569647 -0.02398164
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.080044966 0.121490907 0.029295148
## Lag 2e+05 0.037003690 0.066530111 0.019394031
## Lag 3e+05 -0.004486779 0.031702838 0.004373941
## Lag 4e+05 -0.009391512 -0.004346064 -0.001134474
## Lag 5e+05 -0.012777296 0.013363022 0.011554661
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.030803161 0.243251118 0.40523500
## Lag 2e+05 0.011960223 0.105863478 0.22844447
## Lag 3e+05 0.026745591 0.045064983 0.14820747
## Lag 4e+05 0.012204901 0.018903496 0.06987540
## Lag 5e+05 -0.006690878 0.004921043 0.06899544
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.0000000000 1.000000000 1.00000000
## Lag 1e+05 0.0283752927 0.024720127 0.06487159
## Lag 2e+05 0.0005072978 0.012600543 0.03215306
## Lag 3e+05 0.0030520688 0.001675673 0.02476529
## Lag 4e+05 0.0125244043 0.012850726 0.00188697
## Lag 5e+05 0.0001678782 0.005546016 -0.00351873
## Chain 4
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.002477512 0.024507681 0.028685042
## Lag 2e+05 0.005321265 0.017694707 -0.002648834
## Lag 3e+05 0.015867446 0.004317558 0.020938561
## Lag 4e+05 0.004146365 0.013529262 -0.008109049
## Lag 5e+05 -0.008326474 0.009299334 0.002651890
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.06062381 0.115300649 0.045511124
## Lag 2e+05 0.02020513 0.060570642 0.019922769
## Lag 3e+05 0.01595261 0.038085930 -0.004192395
## Lag 4e+05 0.01550485 0.044592858 0.009279045
## Lag 5e+05 0.03504352 0.008891448 0.016529020
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 -0.003866550 0.24742856 0.40528370
## Lag 2e+05 -0.002117763 0.10277192 0.21757442
## Lag 3e+05 0.012467299 0.03790471 0.13137566
## Lag 4e+05 -0.032220554 0.02720217 0.07607699
## Lag 5e+05 0.002527161 0.02743008 0.06378746
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.012823690 0.008533056 0.0523957983
## Lag 2e+05 0.011082800 -0.001703363 0.0217625174
## Lag 3e+05 0.007599908 0.017096984 0.0275463791
## Lag 4e+05 0.022222900 -0.014001608 0.0146304890
## Lag 5e+05 -0.001937983 0.004786063 -0.0009791839
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.79958 1.58836 -0.08954
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 1.99868 0.38738 0.39279
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -0.54188 0.33954 -0.52294
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## -0.53934 1.41362 -0.14994
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.42395542 0.11220437 0.92865468
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.04564295 0.69847700 0.69447362
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.58790447 0.73420429 0.60101349
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.58964947 0.15747251 0.88081233
## Joint P-value (lower = worse): 0.4022466 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 1.6100 1.7523 -0.2388
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.1954 2.2839 1.2737
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 1.7593 -0.2546 1.6423
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.9860 1.9689 0.7835
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.10739637 0.07972505 0.81127531
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.84504227 0.02237808 0.20276989
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.07852041 0.79904449 0.10051813
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.32415027 0.04896468 0.43333223
## Joint P-value (lower = worse): 0.2916073 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.38389 0.93602 -0.21770
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 1.36932 0.32829 -1.97978
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 1.21998 1.20605 -0.44020
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## -0.77903 -0.02841 0.33475
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.70105677 0.34926228 0.82766518
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.17089951 0.74269005 0.04772835
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.22247260 0.22779795 0.65979379
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.43596328 0.97733399 0.73781267
## Joint P-value (lower = worse): 0.3133133 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.46247 0.34521 1.16078
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.46650 -1.08672 -0.13738
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.06941 0.30893 -0.94283
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.64177 1.53873 1.77456
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.64374380 0.72993910 0.24573211
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.64085625 0.27716185 0.89072885
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.94466572 0.75737460 0.34576705
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.52102428 0.12387068 0.07597025
## Joint P-value (lower = worse): 0.7146153 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 2
## Sample statistics summary:
##
## Iterations = 1e+06:750900000
## Thinning interval = 1e+05
## Number of chains = 4
## Sample size per chain = 7500
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.45803 28.833 0.16647 0.17273
## nodefactor.deg.pers.1 -0.09110 17.518 0.10114 0.10723
## nodefactor.deg.pers.2 0.05450 18.656 0.10771 0.11421
## nodefactor.race..wa.B 0.02753 12.536 0.07237 0.08219
## nodefactor.race..wa.H 0.10950 17.277 0.09975 0.13449
## nodefactor.region.EW 0.10763 16.944 0.09783 0.11332
## nodefactor.region.OW 0.28857 31.407 0.18133 0.18173
## nodematch.race..wa.B 0.06226 4.996 0.02885 0.04013
## nodematch.race..wa.H 0.10582 8.657 0.04998 0.09853
## nodematch.race..wa.O 0.44620 26.329 0.15201 0.15896
## absdiff.sqrt.age -0.66064 28.472 0.16438 0.16708
## nodematch.region 0.61887 28.724 0.16584 0.19329
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -56.500 -18.500 5.000e-01 19.500 56.500
## nodefactor.deg.pers.1 -35.000 -12.000 5.684e-14 12.000 34.000
## nodefactor.deg.pers.2 -36.000 -13.000 0.000e+00 13.000 37.000
## nodefactor.race..wa.B -23.834 -8.834 1.664e-01 8.166 25.166
## nodefactor.race..wa.H -33.844 -11.844 1.560e-01 12.156 34.156
## nodefactor.region.EW -32.561 -11.561 4.392e-01 11.439 33.439
## nodefactor.region.OW -61.131 -21.131 8.694e-01 21.869 61.869
## nodematch.race..wa.B -9.177 -3.177 -1.769e-01 3.823 9.823
## nodematch.race..wa.H -17.300 -5.300 -3.003e-01 5.700 16.700
## nodematch.race..wa.O -50.946 -16.946 5.397e-02 18.054 52.054
## absdiff.sqrt.age -55.851 -19.937 -7.991e-01 18.397 55.638
## nodematch.region -56.350 -19.350 6.500e-01 19.650 56.650
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.pers.1
## edges 1.0000000 0.39648837
## nodefactor.deg.pers.1 0.3964884 1.00000000
## nodefactor.deg.pers.2 0.4214191 0.04164808
## nodefactor.race..wa.B 0.2570854 0.10517644
## nodefactor.race..wa.H 0.3555272 0.16407533
## nodefactor.region.EW 0.3515428 0.14480917
## nodefactor.region.OW 0.5969688 0.21704612
## nodematch.race..wa.B 0.1004038 0.04043360
## nodematch.race..wa.H 0.1330123 0.07283070
## nodematch.race..wa.O 0.8232959 0.31617282
## absdiff.sqrt.age 0.5394191 0.21873317
## nodematch.region 0.6879354 0.27200168
## nodefactor.deg.pers.2 nodefactor.race..wa.B
## edges 0.42141905 0.25708538
## nodefactor.deg.pers.1 0.04164808 0.10517644
## nodefactor.deg.pers.2 1.00000000 0.13765630
## nodefactor.race..wa.B 0.13765630 1.00000000
## nodefactor.race..wa.H 0.13031133 0.03804974
## nodefactor.region.EW 0.14310453 0.05474895
## nodefactor.region.OW 0.26235996 0.11619980
## nodematch.race..wa.B 0.05583424 0.56552649
## nodematch.race..wa.H 0.04535423 -0.04693903
## nodematch.race..wa.O 0.34666948 -0.05187099
## absdiff.sqrt.age 0.22912850 0.13953109
## nodematch.region 0.29230510 0.19124087
## nodefactor.race..wa.H nodefactor.region.EW
## edges 0.35552724 0.351542808
## nodefactor.deg.pers.1 0.16407533 0.144809173
## nodefactor.deg.pers.2 0.13031133 0.143104534
## nodefactor.race..wa.B 0.03804974 0.054748949
## nodefactor.race..wa.H 1.00000000 0.240723145
## nodefactor.region.EW 0.24072314 1.000000000
## nodefactor.region.OW 0.19191207 0.044348937
## nodematch.race..wa.B -0.02279050 0.008648258
## nodematch.race..wa.H 0.59193141 0.127458304
## nodematch.race..wa.O -0.06150487 0.253073027
## absdiff.sqrt.age 0.19456582 0.182865195
## nodematch.region 0.22670976 0.101637830
## nodefactor.region.OW nodematch.race..wa.B
## edges 0.59696880 0.100403849
## nodefactor.deg.pers.1 0.21704612 0.040433602
## nodefactor.deg.pers.2 0.26235996 0.055834243
## nodefactor.race..wa.B 0.11619980 0.565526486
## nodefactor.race..wa.H 0.19191207 -0.022790503
## nodefactor.region.EW 0.04434894 0.008648258
## nodefactor.region.OW 1.00000000 0.037743537
## nodematch.race..wa.B 0.03774354 1.000000000
## nodematch.race..wa.H 0.06389304 0.015351683
## nodematch.race..wa.O 0.50986555 0.017273399
## absdiff.sqrt.age 0.32258179 0.049253721
## nodematch.region 0.35056008 0.083925278
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.13301228 0.82329594
## nodefactor.deg.pers.1 0.07283070 0.31617282
## nodefactor.deg.pers.2 0.04535423 0.34666948
## nodefactor.race..wa.B -0.04693903 -0.05187099
## nodefactor.race..wa.H 0.59193141 -0.06150487
## nodefactor.region.EW 0.12745830 0.25307303
## nodefactor.region.OW 0.06389304 0.50986555
## nodematch.race..wa.B 0.01535168 0.01727340
## nodematch.race..wa.H 1.00000000 0.07408372
## nodematch.race..wa.O 0.07408372 1.00000000
## absdiff.sqrt.age 0.07079624 0.44179515
## nodematch.region 0.08269963 0.56969361
## absdiff.sqrt.age nodematch.region
## edges 0.53941905 0.68793535
## nodefactor.deg.pers.1 0.21873317 0.27200168
## nodefactor.deg.pers.2 0.22912850 0.29230510
## nodefactor.race..wa.B 0.13953109 0.19124087
## nodefactor.race..wa.H 0.19456582 0.22670976
## nodefactor.region.EW 0.18286520 0.10163783
## nodefactor.region.OW 0.32258179 0.35056008
## nodematch.race..wa.B 0.04925372 0.08392528
## nodematch.race..wa.H 0.07079624 0.08269963
## nodematch.race..wa.O 0.44179515 0.56969361
## absdiff.sqrt.age 1.00000000 0.37535985
## nodematch.region 0.37535985 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.024052062 0.054000384 0.0378557233
## Lag 2e+05 0.024971812 0.038570040 0.0175742076
## Lag 3e+05 0.025836535 0.005161105 0.0228455860
## Lag 4e+05 -0.003375954 0.006753667 -0.0002703793
## Lag 5e+05 0.028768135 -0.015417313 0.0157805647
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.064040243 0.129567723 0.090126699
## Lag 2e+05 0.021426520 0.080650049 0.057975653
## Lag 3e+05 0.025926214 0.032962347 0.036605377
## Lag 4e+05 0.001657066 0.027881479 0.014962576
## Lag 5e+05 -0.017159781 -0.004612821 0.004535771
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.015024747 0.26182148 0.42916914
## Lag 2e+05 -0.004134889 0.11255391 0.25629642
## Lag 3e+05 0.009597834 0.05818770 0.16016540
## Lag 4e+05 0.011183471 0.02154639 0.11504843
## Lag 5e+05 0.020651407 0.01240727 0.07183735
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.027162399 0.035922106 0.07363861
## Lag 2e+05 0.004510572 0.014432195 0.03657705
## Lag 3e+05 0.002341037 0.019713826 0.02759075
## Lag 4e+05 -0.018012432 -0.003328509 -0.00804514
## Lag 5e+05 0.030249862 0.004986180 0.01813186
## Chain 2
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.017022164 0.024274392 0.059733900
## Lag 2e+05 0.026596380 0.032320651 0.009540965
## Lag 3e+05 0.002269689 0.007782839 0.023852467
## Lag 4e+05 -0.001020406 0.020413132 0.009300845
## Lag 5e+05 -0.002809718 -0.003526567 0.017644942
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.071658461 0.12284625 0.08540692
## Lag 2e+05 0.040631559 0.07332816 0.04083345
## Lag 3e+05 0.026325684 0.06274198 0.02744378
## Lag 4e+05 -0.001859442 0.04046536 0.02998016
## Lag 5e+05 -0.017674893 0.02754439 0.01121620
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.0000000
## Lag 1e+05 0.014246745 0.2387180098 0.4363786
## Lag 2e+05 0.004794625 0.1040359031 0.2634566
## Lag 3e+05 -0.004059834 0.0409610223 0.1831771
## Lag 4e+05 0.010040528 0.0177793633 0.1457262
## Lag 5e+05 -0.019151986 0.0007663863 0.1093860
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 0.0310669278 0.020292517 0.0794832736
## Lag 2e+05 0.0286114009 -0.005451975 0.0505525329
## Lag 3e+05 0.0032323396 0.006802891 0.0310412694
## Lag 4e+05 -0.0040165885 0.018059290 0.0006139955
## Lag 5e+05 -0.0008810552 0.004884893 -0.0017707985
## Chain 3
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.009247774 0.041998194 0.028584066
## Lag 2e+05 0.009641066 0.002259769 0.011440508
## Lag 3e+05 -0.011145613 0.017476885 0.008129774
## Lag 4e+05 0.012606191 0.021919276 -0.002149665
## Lag 5e+05 0.004485367 0.005536555 0.006824544
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.000000000
## Lag 1e+05 0.08632082 0.13296504 0.080217924
## Lag 2e+05 0.02764798 0.06276261 0.043248986
## Lag 3e+05 0.01039477 0.03320257 0.004373299
## Lag 4e+05 -0.00169169 0.05272205 0.025832208
## Lag 5e+05 0.03020837 0.01973644 0.019175688
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.0000000
## Lag 1e+05 0.029552140 0.25986989 0.4297156
## Lag 2e+05 -0.019552573 0.12941025 0.2593717
## Lag 3e+05 -0.009683967 0.06651930 0.1743791
## Lag 4e+05 0.005647441 0.03225285 0.1187260
## Lag 5e+05 -0.017248669 0.02770114 0.0866218
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.034015573 0.034863441 0.070262606
## Lag 2e+05 0.019828653 -0.002523521 0.024412264
## Lag 3e+05 -0.006316116 0.010650837 0.010151481
## Lag 4e+05 0.026151658 0.001449815 0.015468708
## Lag 5e+05 0.004162886 -0.011952338 0.002209284
## Chain 4
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.008276705 0.025611820 0.0195942747
## Lag 2e+05 0.015283528 0.019589735 0.0177242739
## Lag 3e+05 0.001192975 -0.007435216 -0.0028708724
## Lag 4e+05 0.013201111 0.003629732 -0.0008658751
## Lag 5e+05 0.001560119 0.002653204 -0.0005874572
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.084139766 0.14257486 0.07532788
## Lag 2e+05 0.055354918 0.05933350 0.04178701
## Lag 3e+05 0.003146485 0.05188458 0.01247795
## Lag 4e+05 0.012858193 0.03315969 0.01063902
## Lag 5e+05 0.020309903 0.02535236 -0.01399067
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.003696308 0.269817675 0.41352804
## Lag 2e+05 0.001378425 0.112866920 0.24636944
## Lag 3e+05 -0.004434457 0.057725060 0.16115714
## Lag 4e+05 -0.006366795 0.027932473 0.12550506
## Lag 5e+05 0.005293304 0.007851679 0.07839368
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.015653330 0.01419750 0.09009810
## Lag 2e+05 0.007121061 0.03461569 0.03423587
## Lag 3e+05 -0.003347020 0.01069876 0.03704391
## Lag 4e+05 0.001358528 -0.02914666 0.02003756
## Lag 5e+05 0.003576397 -0.01158922 0.01669649
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.21841 -2.45416 2.10726
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.85085 -1.04380 0.07583
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -0.48578 -1.63939 -1.10258
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## -0.12695 -1.73311 -0.66777
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.82710947 0.01412153 0.03509535
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.39485398 0.29657825 0.93955646
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.62712655 0.10113171 0.27020821
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.89897962 0.08307545 0.50427789
## Joint P-value (lower = worse): 0.008870003 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.64256 0.05730 -0.07648
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.83897 1.10885 0.46038
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -0.23212 -0.74111 1.87500
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## -0.83080 0.65266 -0.52087
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.52050956 0.95430646 0.93903423
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.40148489 0.26749491 0.64524185
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.81644669 0.45862856 0.06079225
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.40608866 0.51397297 0.60245477
## Joint P-value (lower = worse): 0.3753299 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.103502 0.974247 -1.455742
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.102745 -0.162152 1.256171
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -1.341443 1.436556 -0.368879
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## -0.044091 -0.006629 -0.295059
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.9175645 0.3299339 0.1454641
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.9181650 0.8711862 0.2090539
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.1797766 0.1508442 0.7122181
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.9648318 0.9947112 0.7679487
## Joint P-value (lower = worse): 0.4244694 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.7836 -1.7679 -0.2663
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.1298 -0.3069 0.8221
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -1.2399 -0.3947 -0.1239
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## -0.4874 0.6382 -0.8766
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.4332549 0.0770731 0.7900386
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.8967081 0.7588875 0.4109989
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.2149976 0.6930332 0.9014173
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.6259732 0.5233468 0.3806895
## Joint P-value (lower = worse): 0.7891915 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 3
## Sample statistics summary:
##
## Iterations = 1e+06:750900000
## Thinning interval = 1e+05
## Number of chains = 4
## Sample size per chain = 7500
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.51840 28.665 0.16550 0.18018
## nodefactor.deg.pers.1 -0.05463 17.389 0.10040 0.10664
## nodefactor.deg.pers.2 0.35447 18.648 0.10767 0.11536
## nodefactor.race..wa.B 0.33957 12.583 0.07265 0.08426
## nodefactor.race..wa.H 0.11440 17.437 0.10067 0.14915
## nodefactor.region.EW -0.01177 17.439 0.10068 0.13162
## nodefactor.region.OW -0.22193 32.146 0.18560 0.19477
## nodematch.race..wa.B 0.09659 5.002 0.02888 0.04173
## nodematch.race..wa.H 0.19535 8.785 0.05072 0.10502
## nodematch.race..wa.O 0.17950 26.249 0.15155 0.16050
## absdiff.sqrt.age 1.27454 28.510 0.16460 0.17314
## nodematch.region 0.07393 29.484 0.17023 0.20014
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -55.500 -19.500 5.000e-01 19.500 56.500
## nodefactor.deg.pers.1 -34.000 -12.000 5.684e-14 12.000 34.000
## nodefactor.deg.pers.2 -36.000 -12.000 0.000e+00 13.000 37.000
## nodefactor.race..wa.B -23.834 -7.834 1.664e-01 9.166 25.166
## nodefactor.race..wa.H -33.844 -11.844 1.560e-01 12.156 34.156
## nodefactor.region.EW -34.561 -11.561 4.392e-01 11.439 34.439
## nodefactor.region.OW -63.131 -22.131 -1.306e-01 21.869 62.869
## nodematch.race..wa.B -9.177 -3.177 -1.769e-01 3.823 9.823
## nodematch.race..wa.H -16.300 -6.300 -3.003e-01 5.700 17.700
## nodematch.race..wa.O -50.946 -17.946 5.397e-02 18.054 51.054
## absdiff.sqrt.age -54.585 -17.935 1.166e+00 20.278 57.793
## nodematch.region -58.400 -19.400 6.000e-01 19.600 58.600
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.pers.1
## edges 1.00000000 0.38399568
## nodefactor.deg.pers.1 0.38399568 1.00000000
## nodefactor.deg.pers.2 0.42501155 0.03640769
## nodefactor.race..wa.B 0.24774439 0.08617435
## nodefactor.race..wa.H 0.35870088 0.16465904
## nodefactor.region.EW 0.33593418 0.15404976
## nodefactor.region.OW 0.58619580 0.19227547
## nodematch.race..wa.B 0.09784436 0.03035006
## nodematch.race..wa.H 0.13371730 0.06714906
## nodematch.race..wa.O 0.82160519 0.30489410
## absdiff.sqrt.age 0.54425889 0.21113757
## nodematch.region 0.77440701 0.29616627
## nodefactor.deg.pers.2 nodefactor.race..wa.B
## edges 0.42501155 0.24774439
## nodefactor.deg.pers.1 0.03640769 0.08617435
## nodefactor.deg.pers.2 1.00000000 0.12631843
## nodefactor.race..wa.B 0.12631843 1.00000000
## nodefactor.race..wa.H 0.13463515 0.02963756
## nodefactor.region.EW 0.13874076 0.02076034
## nodefactor.region.OW 0.25076910 0.10817544
## nodematch.race..wa.B 0.05682036 0.57387839
## nodematch.race..wa.H 0.04206318 -0.05279207
## nodematch.race..wa.O 0.35164097 -0.05790142
## absdiff.sqrt.age 0.23902310 0.13758574
## nodematch.region 0.32592421 0.20578290
## nodefactor.race..wa.H nodefactor.region.EW
## edges 0.35870088 0.335934181
## nodefactor.deg.pers.1 0.16465904 0.154049763
## nodefactor.deg.pers.2 0.13463515 0.138740755
## nodefactor.race..wa.B 0.02963756 0.020760339
## nodefactor.race..wa.H 1.00000000 0.240282054
## nodefactor.region.EW 0.24028205 1.000000000
## nodefactor.region.OW 0.19200161 0.021842192
## nodematch.race..wa.B -0.02915976 -0.008564537
## nodematch.race..wa.H 0.59579226 0.135234029
## nodematch.race..wa.O -0.05935191 0.244511190
## absdiff.sqrt.age 0.19489804 0.182554929
## nodematch.region 0.26420955 0.163720511
## nodefactor.region.OW nodematch.race..wa.B
## edges 0.58619580 0.097844355
## nodefactor.deg.pers.1 0.19227547 0.030350062
## nodefactor.deg.pers.2 0.25076910 0.056820364
## nodefactor.race..wa.B 0.10817544 0.573878388
## nodefactor.race..wa.H 0.19200161 -0.029159757
## nodefactor.region.EW 0.02184219 -0.008564537
## nodefactor.region.OW 1.00000000 0.037933413
## nodematch.race..wa.B 0.03793341 1.000000000
## nodematch.race..wa.H 0.06482497 0.001588357
## nodematch.race..wa.O 0.50028138 0.013709061
## absdiff.sqrt.age 0.31777997 0.052936940
## nodematch.region 0.42641814 0.094613370
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.133717301 0.82160519
## nodefactor.deg.pers.1 0.067149055 0.30489410
## nodefactor.deg.pers.2 0.042063182 0.35164097
## nodefactor.race..wa.B -0.052792065 -0.05790142
## nodefactor.race..wa.H 0.595792258 -0.05935191
## nodefactor.region.EW 0.135234029 0.24451119
## nodefactor.region.OW 0.064824973 0.50028138
## nodematch.race..wa.B 0.001588357 0.01370906
## nodematch.race..wa.H 1.000000000 0.07696453
## nodematch.race..wa.O 0.076964528 1.00000000
## absdiff.sqrt.age 0.075164280 0.44949575
## nodematch.region 0.101982126 0.64062944
## absdiff.sqrt.age nodematch.region
## edges 0.54425889 0.77440701
## nodefactor.deg.pers.1 0.21113757 0.29616627
## nodefactor.deg.pers.2 0.23902310 0.32592421
## nodefactor.race..wa.B 0.13758574 0.20578290
## nodefactor.race..wa.H 0.19489804 0.26420955
## nodefactor.region.EW 0.18255493 0.16372051
## nodefactor.region.OW 0.31777997 0.42641814
## nodematch.race..wa.B 0.05293694 0.09461337
## nodematch.race..wa.H 0.07516428 0.10198213
## nodematch.race..wa.O 0.44949575 0.64062944
## absdiff.sqrt.age 1.00000000 0.42177351
## nodematch.region 0.42177351 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.00000000 1.0000000000 1.000000000
## Lag 1e+05 0.06220383 0.0350605655 0.044678639
## Lag 2e+05 0.02533804 0.0154370737 0.024817117
## Lag 3e+05 0.02108077 0.0009837513 0.006744630
## Lag 4e+05 0.01535455 0.0116211535 0.016466743
## Lag 5e+05 0.02133586 0.0045899808 0.002385131
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.000000000
## Lag 1e+05 0.10674681 0.18893379 0.138552842
## Lag 2e+05 0.02791681 0.11284395 0.066184846
## Lag 3e+05 0.03252739 0.08592809 0.002231839
## Lag 4e+05 0.01517966 0.05617219 0.026410576
## Lag 5e+05 0.02316481 0.04479136 0.038116096
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.0000000
## Lag 1e+05 0.049647628 0.28605364 0.4775809
## Lag 2e+05 0.020347787 0.12883278 0.3072051
## Lag 3e+05 0.006861189 0.08577687 0.2130773
## Lag 4e+05 -0.007748032 0.06319894 0.1564303
## Lag 5e+05 0.003726987 0.03801912 0.1184951
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.077283752 0.030048741 0.10407857
## Lag 2e+05 0.031689626 0.023063790 0.04001199
## Lag 3e+05 0.006619431 -0.005376737 0.03504363
## Lag 4e+05 0.010793947 0.008461907 0.01848473
## Lag 5e+05 0.004235001 0.013740078 0.01657965
## Chain 2
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.0000000000 1.00000000 1.000000e+00
## Lag 1e+05 0.0305052020 0.02744812 4.665124e-02
## Lag 2e+05 -0.0038918233 0.02465567 1.521890e-02
## Lag 3e+05 -0.0003688082 0.02309760 1.674629e-02
## Lag 4e+05 -0.0080789483 0.01240578 -8.967872e-05
## Lag 5e+05 0.0001462908 0.02604722 -6.319864e-03
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.109107958 0.15187852 0.16820278
## Lag 2e+05 0.065676219 0.06251518 0.08120426
## Lag 3e+05 0.035814054 0.06656035 0.05407095
## Lag 4e+05 0.013520929 0.05966982 0.03642571
## Lag 5e+05 0.002236154 0.04688177 0.01769618
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.0000000
## Lag 1e+05 0.025917098 0.30082052 0.4708776
## Lag 2e+05 0.012764925 0.14546498 0.3027323
## Lag 3e+05 -0.018991770 0.06333764 0.2201405
## Lag 4e+05 -0.010932101 0.03614071 0.1692990
## Lag 5e+05 0.001179983 0.02167846 0.1407477
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.0000000000 1.00000000 1.000000000
## Lag 1e+05 0.0327926759 0.02102748 0.080815352
## Lag 2e+05 -0.0027309962 0.01617958 0.025075729
## Lag 3e+05 -0.0019704912 0.01552050 0.011727783
## Lag 4e+05 -0.0069192194 0.01481039 0.013583220
## Lag 5e+05 -0.0002764306 0.02020466 0.007843556
## Chain 3
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.046551841 0.063582272 0.068862364
## Lag 2e+05 0.012125256 0.011170368 0.013934766
## Lag 3e+05 0.022397334 -0.005106113 0.007892261
## Lag 4e+05 0.014106640 0.006317687 0.018756481
## Lag 5e+05 0.002076271 0.012825112 -0.004112908
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.105320329 0.17807423 0.16931123
## Lag 2e+05 0.051209443 0.11209728 0.07354266
## Lag 3e+05 0.028119630 0.08303828 0.04265387
## Lag 4e+05 0.004833264 0.04775836 0.03570045
## Lag 5e+05 -0.005595435 0.04768744 0.02045146
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.0000000
## Lag 1e+05 0.038156218 0.28312800 0.4836928
## Lag 2e+05 -0.009350220 0.13055685 0.3067842
## Lag 3e+05 0.007257242 0.04927485 0.2076350
## Lag 4e+05 0.004004372 0.04059814 0.1529188
## Lag 5e+05 -0.014867289 0.02236485 0.1015655
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.051885841 0.03795619 0.092612202
## Lag 2e+05 -0.005402629 0.01094587 0.044229978
## Lag 3e+05 0.012035302 0.02482609 0.027849559
## Lag 4e+05 0.001153699 0.02650749 0.020645741
## Lag 5e+05 -0.003692265 -0.00505906 0.008239393
## Chain 4
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.0000000000 1.0000000000
## Lag 1e+05 0.029871805 0.0280281807 0.0673487835
## Lag 2e+05 0.015794614 0.0006529373 0.0282649251
## Lag 3e+05 0.029737904 0.0077947505 0.0046384728
## Lag 4e+05 -0.005641059 0.0135976927 0.0007185074
## Lag 5e+05 0.008785673 0.0069560540 0.0054559291
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.099367980 0.16958050 0.15062097
## Lag 2e+05 0.035730872 0.11133924 0.06470266
## Lag 3e+05 0.031683242 0.09413500 0.05266533
## Lag 4e+05 0.008030988 0.05750237 0.03255680
## Lag 5e+05 0.006649107 0.04855747 0.02269723
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.0000000
## Lag 1e+05 0.036551179 0.29423197 0.4926052
## Lag 2e+05 0.024983721 0.12107652 0.3221774
## Lag 3e+05 0.015833446 0.06002456 0.2287679
## Lag 4e+05 -0.015390044 0.03267422 0.1775790
## Lag 5e+05 -0.002303256 0.01448736 0.1417729
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.038977254 0.011308497 0.08248003
## Lag 2e+05 0.016680547 -0.015159726 0.03803942
## Lag 3e+05 0.015418082 -0.004771892 0.04409279
## Lag 4e+05 -0.005090396 0.013661230 0.00478046
## Lag 5e+05 0.005293607 -0.006526436 0.01588795
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.3949 -3.2065 1.1316
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -1.2057 -0.9201 0.4422
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.7761 -1.5657 -0.2037
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 1.4228 0.7325 0.2896
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.692946433 0.001343476 0.257814616
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.227938710 0.357539606 0.658376971
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.437700483 0.117419238 0.838599006
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.154783692 0.463838623 0.772118828
## Joint P-value (lower = worse): 0.08174063 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.11281 -1.10205 0.23002
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.22781 0.27324 -1.47206
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.72915 -0.27298 0.54992
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## -0.03706 -1.38485 -0.33053
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.9101840 0.2704406 0.8180765
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.8197973 0.7846656 0.1410053
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.4659074 0.7848649 0.5823772
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.9704346 0.1660971 0.7409973
## Joint P-value (lower = worse): 0.8365099 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.4962 1.9291 0.8754
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.1795 -1.4637 1.9883
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.4042 -0.3858 -1.6740
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 1.4607 -0.8258 0.4092
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.61974263 0.05371265 0.38134564
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.85757987 0.14326667 0.04677315
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.68606301 0.69966347 0.09413050
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.14408865 0.40894410 0.68237589
## Joint P-value (lower = worse): 0.03813256 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.4838 -0.3931 0.1295
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.0687 0.7065 0.8680
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 1.0167 -0.5367 1.0159
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.5088 1.9821 0.4061
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.62852101 0.69425144 0.89699070
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.94523089 0.47986068 0.38537423
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.30930935 0.59146731 0.30969995
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.61089316 0.04747295 0.68467284
## Joint P-value (lower = worse): 0.6753301 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 4
## Sample statistics summary:
##
## Iterations = 1e+06:750900000
## Thinning interval = 1e+05
## Number of chains = 4
## Sample size per chain = 7500
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.904667 28.849 0.16656 0.17656
## nodefactor.deg.pers.1 0.001933 17.576 0.10148 0.10834
## nodefactor.deg.pers.2 0.737067 18.671 0.10780 0.11572
## nodefactor.race..wa.B 0.353267 12.546 0.07243 0.08682
## nodefactor.race..wa.H 0.868433 17.371 0.10029 0.14074
## nodefactor.region.EW 0.981367 17.275 0.09974 0.12355
## nodefactor.region.OW 0.633000 32.329 0.18665 0.19414
## nodematch.race..wa.B 0.079594 4.991 0.02881 0.04245
## nodematch.race..wa.H 0.369688 8.701 0.05023 0.10045
## nodematch.race..wa.O 0.018437 26.239 0.15149 0.15951
## absdiff.sqrt.age 0.424363 28.575 0.16498 0.17463
## nodematch.region 1.276100 29.537 0.17053 0.19718
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -56.500 -18.500 5.000e-01 20.500 57.500
## nodefactor.deg.pers.1 -35.000 -12.000 5.684e-14 12.000 34.000
## nodefactor.deg.pers.2 -36.000 -12.000 1.000e+00 13.000 37.000
## nodefactor.race..wa.B -23.834 -7.834 1.664e-01 9.166 25.166
## nodefactor.race..wa.H -32.844 -10.844 1.156e+00 13.156 35.156
## nodefactor.region.EW -32.561 -10.561 1.439e+00 12.439 34.439
## nodefactor.region.OW -62.131 -21.131 8.694e-01 22.869 63.869
## nodematch.race..wa.B -9.177 -3.177 -1.769e-01 3.823 9.823
## nodematch.race..wa.H -16.300 -5.300 6.997e-01 6.700 17.700
## nodematch.race..wa.O -50.946 -17.946 5.397e-02 18.054 51.054
## absdiff.sqrt.age -54.787 -19.013 1.352e-01 19.490 57.055
## nodematch.region -56.400 -18.400 1.600e+00 21.600 59.600
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.pers.1
## edges 1.0000000 0.40215756
## nodefactor.deg.pers.1 0.4021576 1.00000000
## nodefactor.deg.pers.2 0.4226635 0.04517507
## nodefactor.race..wa.B 0.2674942 0.10940310
## nodefactor.race..wa.H 0.3544402 0.16328221
## nodefactor.region.EW 0.3310516 0.14209824
## nodefactor.region.OW 0.5899259 0.20719542
## nodematch.race..wa.B 0.1154583 0.05193147
## nodematch.race..wa.H 0.1267458 0.06577146
## nodematch.race..wa.O 0.8225259 0.32257573
## absdiff.sqrt.age 0.5434560 0.22070912
## nodematch.region 0.7804439 0.31342673
## nodefactor.deg.pers.2 nodefactor.race..wa.B
## edges 0.42266350 0.26749422
## nodefactor.deg.pers.1 0.04517507 0.10940310
## nodefactor.deg.pers.2 1.00000000 0.13589388
## nodefactor.race..wa.B 0.13589388 1.00000000
## nodefactor.race..wa.H 0.12629004 0.04061145
## nodefactor.region.EW 0.12060504 0.03374731
## nodefactor.region.OW 0.26131962 0.12127390
## nodematch.race..wa.B 0.05724448 0.56322259
## nodematch.race..wa.H 0.04347266 -0.04575079
## nodematch.race..wa.O 0.35323855 -0.04037836
## absdiff.sqrt.age 0.22841373 0.14445038
## nodematch.region 0.33447636 0.21667330
## nodefactor.race..wa.H nodefactor.region.EW
## edges 0.35444019 0.33105164
## nodefactor.deg.pers.1 0.16328221 0.14209824
## nodefactor.deg.pers.2 0.12629004 0.12060504
## nodefactor.race..wa.B 0.04061145 0.03374731
## nodefactor.race..wa.H 1.00000000 0.22976122
## nodefactor.region.EW 0.22976122 1.00000000
## nodefactor.region.OW 0.19650166 0.02207586
## nodematch.race..wa.B -0.01766152 0.01377097
## nodematch.race..wa.H 0.59551474 0.11313239
## nodematch.race..wa.O -0.06397370 0.24010048
## absdiff.sqrt.age 0.19328699 0.17725713
## nodematch.region 0.26681670 0.16010060
## nodefactor.region.OW nodematch.race..wa.B
## edges 0.58992585 0.11545830
## nodefactor.deg.pers.1 0.20719542 0.05193147
## nodefactor.deg.pers.2 0.26131962 0.05724448
## nodefactor.race..wa.B 0.12127390 0.56322259
## nodefactor.race..wa.H 0.19650166 -0.01766152
## nodefactor.region.EW 0.02207586 0.01377097
## nodefactor.region.OW 1.00000000 0.04138174
## nodematch.race..wa.B 0.04138174 1.00000000
## nodematch.race..wa.H 0.07527558 0.01335103
## nodematch.race..wa.O 0.50313834 0.03133777
## absdiff.sqrt.age 0.32049135 0.06609188
## nodematch.region 0.43024193 0.09302463
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.12674579 0.82252589
## nodefactor.deg.pers.1 0.06577146 0.32257573
## nodefactor.deg.pers.2 0.04347266 0.35323855
## nodefactor.race..wa.B -0.04575079 -0.04037836
## nodefactor.race..wa.H 0.59551474 -0.06397370
## nodefactor.region.EW 0.11313239 0.24010048
## nodefactor.region.OW 0.07527558 0.50313834
## nodematch.race..wa.B 0.01335103 0.03133777
## nodematch.race..wa.H 1.00000000 0.06685340
## nodematch.race..wa.O 0.06685340 1.00000000
## absdiff.sqrt.age 0.06983316 0.44711786
## nodematch.region 0.09698462 0.64358636
## absdiff.sqrt.age nodematch.region
## edges 0.54345602 0.78044391
## nodefactor.deg.pers.1 0.22070912 0.31342673
## nodefactor.deg.pers.2 0.22841373 0.33447636
## nodefactor.race..wa.B 0.14445038 0.21667330
## nodefactor.race..wa.H 0.19328699 0.26681670
## nodefactor.region.EW 0.17725713 0.16010060
## nodefactor.region.OW 0.32049135 0.43024193
## nodematch.race..wa.B 0.06609188 0.09302463
## nodematch.race..wa.H 0.06983316 0.09698462
## nodematch.race..wa.O 0.44711786 0.64358636
## absdiff.sqrt.age 1.00000000 0.41959254
## nodematch.region 0.41959254 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.041624691 0.0593903690 0.060468513
## Lag 2e+05 0.011625278 -0.0016632281 0.002809922
## Lag 3e+05 -0.007458865 0.0064065666 -0.003487079
## Lag 4e+05 0.009532716 -0.0009910668 0.002548369
## Lag 5e+05 0.005865611 0.0022584183 -0.010573712
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.114066198 0.17217240 0.147536410
## Lag 2e+05 0.061762167 0.10517739 0.074204542
## Lag 3e+05 0.036641904 0.09458600 0.042500588
## Lag 4e+05 0.015791979 0.05820982 0.016202550
## Lag 5e+05 0.006788532 0.03352549 -0.008992718
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.0000000
## Lag 1e+05 0.028855532 0.29008051 0.4809439
## Lag 2e+05 0.010946467 0.12868243 0.3256160
## Lag 3e+05 0.006519348 0.08094263 0.2400005
## Lag 4e+05 -0.001311151 0.04029983 0.1756302
## Lag 5e+05 0.016732670 0.01990022 0.1312537
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.046913111 0.040173748 0.093339650
## Lag 2e+05 0.010288621 0.022254332 0.030856559
## Lag 3e+05 -0.019108636 0.016011001 0.002490887
## Lag 4e+05 0.003537071 0.008178554 0.022628391
## Lag 5e+05 -0.005241645 0.021477294 -0.004396471
## Chain 2
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.04316422 0.055456189 0.056354793
## Lag 2e+05 -0.01280681 0.021698736 0.026206129
## Lag 3e+05 0.01376627 0.003428698 -0.006797791
## Lag 4e+05 0.01133957 0.013703837 0.011513771
## Lag 5e+05 0.02250675 0.000839326 0.016834183
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.095181299 0.16323055 0.131050092
## Lag 2e+05 0.046204653 0.10326784 0.066941598
## Lag 3e+05 0.028219071 0.05362845 0.039046258
## Lag 4e+05 -0.007705089 0.05185593 0.015758161
## Lag 5e+05 0.014562066 0.02588716 -0.003535345
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.019413924 0.298980023 0.45931975
## Lag 2e+05 0.002369324 0.132066907 0.29955175
## Lag 3e+05 0.021766656 0.059160117 0.21272357
## Lag 4e+05 0.003208790 0.013357386 0.14867105
## Lag 5e+05 0.009075327 0.006006866 0.09951799
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.044983748 0.042375022 0.087301657
## Lag 2e+05 -0.009496034 -0.004389642 0.008503415
## Lag 3e+05 0.010634398 0.013448836 0.016432363
## Lag 4e+05 0.008006926 0.003564373 0.016298018
## Lag 5e+05 0.015296296 -0.001995309 0.037796575
## Chain 3
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0532928045 0.056001356 0.068735678
## Lag 2e+05 0.0129138070 0.001695288 0.022701676
## Lag 3e+05 0.0078168603 0.010728769 0.001379720
## Lag 4e+05 -0.0018985795 0.020715960 -0.001347159
## Lag 5e+05 0.0005122246 0.016091163 -0.001131561
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.11482857 0.16930471 0.16200613
## Lag 2e+05 0.04634291 0.08523461 0.05091585
## Lag 3e+05 0.03065976 0.06331943 0.02910192
## Lag 4e+05 0.01813439 0.05083199 0.02745151
## Lag 5e+05 0.02699848 0.04080523 -0.01058780
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.062936708 0.31418958 0.46267899
## Lag 2e+05 0.013797560 0.15198573 0.28615754
## Lag 3e+05 0.008223780 0.08995401 0.20408323
## Lag 4e+05 -0.009756352 0.04165097 0.13637103
## Lag 5e+05 0.003369755 0.01185447 0.09462516
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.069250766 0.056458622 0.100016204
## Lag 2e+05 0.011590983 0.002755042 0.038778661
## Lag 3e+05 0.002091141 0.013621258 0.025845065
## Lag 4e+05 -0.007963031 -0.009791474 -0.007523829
## Lag 5e+05 -0.007180092 -0.004984341 -0.002826501
## Chain 4
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.036271888 0.031597344 0.056681971
## Lag 2e+05 -0.002059562 0.018461830 0.008978490
## Lag 3e+05 0.001432062 0.023533730 0.022344973
## Lag 4e+05 0.016510239 0.011220150 -0.010596596
## Lag 5e+05 0.005755383 -0.009242719 0.007556281
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.10222325 0.16366100 0.14463952
## Lag 2e+05 0.04416828 0.09262306 0.05716520
## Lag 3e+05 0.01517042 0.05175988 0.02472843
## Lag 4e+05 0.01252703 0.04427914 0.02859578
## Lag 5e+05 0.02156777 0.05015390 0.01763720
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000e+00 1.00000000 1.0000000
## Lag 1e+05 4.432029e-02 0.30252575 0.4784087
## Lag 2e+05 3.591745e-03 0.15263285 0.3198533
## Lag 3e+05 1.168675e-02 0.07878900 0.2226296
## Lag 4e+05 -6.403203e-05 0.04746359 0.1622009
## Lag 5e+05 8.240443e-03 0.02462456 0.1316067
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0435847666 0.045299304 0.093451443
## Lag 2e+05 -0.0008639228 0.024208113 0.013942484
## Lag 3e+05 0.0042066333 0.016440329 0.002953556
## Lag 4e+05 0.0142984117 0.006341455 0.028814448
## Lag 5e+05 0.0035867607 -0.011214900 0.011551280
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -1.0280 -0.3314 0.1602
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.4177 0.8149 -1.2736
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -1.0284 1.7220 1.1790
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## -0.7612 -1.2776 -0.9263
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.3039610 0.7403675 0.8727611
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.6761736 0.4151167 0.2027903
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.3037630 0.0850700 0.2384001
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.4465649 0.2013996 0.3542749
## Joint P-value (lower = worse): 0.3902869 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.35154 2.16077 -1.55987
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.49424 0.45693 0.84955
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.03653 1.31006 0.12678
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.20819 -0.53406 0.36005
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.72518076 0.03071341 0.11879071
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.62113854 0.64772222 0.39557677
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.97085799 0.19017681 0.89911708
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.83508257 0.59330301 0.71881234
## Joint P-value (lower = worse): 0.553489 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.2605 -0.3780 -0.2813
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.1624 0.8225 1.3409
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -1.8779 -0.5090 0.7234
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## -0.1532 -0.2940 0.8151
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.79447101 0.70541093 0.77850462
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.87096705 0.41077704 0.17994958
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.06039959 0.61075772 0.46943187
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.87826472 0.76877595 0.41500545
## Joint P-value (lower = worse): 0.6333987 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.4907 -0.5469 0.2365
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.8166 -0.5197 -0.4536
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.7637 0.8135 -1.0459
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## -1.0516 0.8479 -0.1406
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.6236120 0.5844499 0.8130597
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.4141605 0.6032949 0.6501001
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.4450272 0.4159135 0.2956045
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.2929893 0.3965180 0.8881807
## Joint P-value (lower = worse): 0.8541978 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 5
## Sample statistics summary:
##
## Iterations = 1e+06:750900000
## Thinning interval = 1e+05
## Number of chains = 4
## Sample size per chain = 7500
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 2.7057 28.754 0.16601 0.18834
## nodefactor.deg.pers.1 -0.3454 17.527 0.10119 0.12160
## nodefactor.deg.pers.2 0.9618 18.584 0.10729 0.12872
## nodefactor.race..wa.B 0.5597 12.412 0.07166 0.08863
## nodefactor.race..wa.H 1.3317 17.219 0.09941 0.17002
## nodefactor.region.EW -0.1485 18.288 0.10559 0.16208
## nodefactor.region.OW 1.9396 33.217 0.19178 0.20438
## nodematch.race..wa.B 0.3895 4.957 0.02862 0.04625
## nodematch.race..wa.H 0.2140 8.607 0.04969 0.12960
## nodematch.race..wa.O 1.4588 26.291 0.15179 0.16938
## absdiff.sqrt.age 3.3084 28.550 0.16483 0.18439
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -53.500 -16.500 2.500e+00 22.500 58.50
## nodefactor.deg.pers.1 -35.000 -12.000 5.684e-14 11.000 34.00
## nodefactor.deg.pers.2 -36.000 -12.000 1.000e+00 14.000 37.00
## nodefactor.race..wa.B -23.834 -7.834 1.664e-01 9.166 25.17
## nodefactor.race..wa.H -32.844 -9.844 1.156e+00 13.156 35.16
## nodefactor.region.EW -35.561 -11.561 4.392e-01 12.439 36.44
## nodefactor.region.OW -62.131 -20.131 1.869e+00 23.869 67.87
## nodematch.race..wa.B -9.177 -3.177 -1.769e-01 3.823 10.82
## nodematch.race..wa.H -16.300 -5.300 -3.003e-01 5.700 17.70
## nodematch.race..wa.O -49.946 -15.946 1.054e+00 19.054 53.05
## absdiff.sqrt.age -51.924 -16.248 3.153e+00 22.522 59.59
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.pers.1
## edges 1.0000000 0.40201216
## nodefactor.deg.pers.1 0.4020122 1.00000000
## nodefactor.deg.pers.2 0.4221407 0.04107354
## nodefactor.race..wa.B 0.2587356 0.10477224
## nodefactor.race..wa.H 0.3533623 0.16735795
## nodefactor.region.EW 0.3211884 0.15361795
## nodefactor.region.OW 0.5701391 0.19730788
## nodematch.race..wa.B 0.1053215 0.04315490
## nodematch.race..wa.H 0.1274991 0.07093768
## nodematch.race..wa.O 0.8238238 0.32030312
## absdiff.sqrt.age 0.5504959 0.22104112
## nodefactor.deg.pers.2 nodefactor.race..wa.B
## edges 0.42214067 0.25873564
## nodefactor.deg.pers.1 0.04107354 0.10477224
## nodefactor.deg.pers.2 1.00000000 0.13944681
## nodefactor.race..wa.B 0.13944681 1.00000000
## nodefactor.race..wa.H 0.13012586 0.04366917
## nodefactor.region.EW 0.12377313 0.02342191
## nodefactor.region.OW 0.25483306 0.11008695
## nodematch.race..wa.B 0.06832952 0.56322944
## nodematch.race..wa.H 0.04563004 -0.03981234
## nodematch.race..wa.O 0.34860829 -0.04787222
## absdiff.sqrt.age 0.23347232 0.14205760
## nodefactor.race..wa.H nodefactor.region.EW
## edges 0.35336230 0.3211884021
## nodefactor.deg.pers.1 0.16735795 0.1536179454
## nodefactor.deg.pers.2 0.13012586 0.1237731284
## nodefactor.race..wa.B 0.04366917 0.0234219111
## nodefactor.race..wa.H 1.00000000 0.2256241155
## nodefactor.region.EW 0.22562412 1.0000000000
## nodefactor.region.OW 0.18806983 -0.0020146326
## nodematch.race..wa.B -0.01138609 0.0007166267
## nodematch.race..wa.H 0.58954132 0.1172160461
## nodematch.race..wa.O -0.06450880 0.2357620080
## absdiff.sqrt.age 0.20020377 0.1752323441
## nodefactor.region.OW nodematch.race..wa.B
## edges 0.570139101 0.1053214896
## nodefactor.deg.pers.1 0.197307877 0.0431549045
## nodefactor.deg.pers.2 0.254833058 0.0683295200
## nodefactor.race..wa.B 0.110086951 0.5632294434
## nodefactor.race..wa.H 0.188069825 -0.0113860901
## nodefactor.region.EW -0.002014633 0.0007166267
## nodefactor.region.OW 1.000000000 0.0368384515
## nodematch.race..wa.B 0.036838451 1.0000000000
## nodematch.race..wa.H 0.065683855 0.0153459308
## nodematch.race..wa.O 0.485656888 0.0173318676
## absdiff.sqrt.age 0.313881855 0.0573839788
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.12749908 0.82382379
## nodefactor.deg.pers.1 0.07093768 0.32030312
## nodefactor.deg.pers.2 0.04563004 0.34860829
## nodefactor.race..wa.B -0.03981234 -0.04787222
## nodefactor.race..wa.H 0.58954132 -0.06450880
## nodefactor.region.EW 0.11721605 0.23576201
## nodefactor.region.OW 0.06568385 0.48565689
## nodematch.race..wa.B 0.01534593 0.01733187
## nodematch.race..wa.H 1.00000000 0.06980539
## nodematch.race..wa.O 0.06980539 1.00000000
## absdiff.sqrt.age 0.06801337 0.44912912
## absdiff.sqrt.age
## edges 0.55049590
## nodefactor.deg.pers.1 0.22104112
## nodefactor.deg.pers.2 0.23347232
## nodefactor.race..wa.B 0.14205760
## nodefactor.race..wa.H 0.20020377
## nodefactor.region.EW 0.17523234
## nodefactor.region.OW 0.31388185
## nodematch.race..wa.B 0.05738398
## nodematch.race..wa.H 0.06801337
## nodematch.race..wa.O 0.44912912
## absdiff.sqrt.age 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.095740655 0.128660558 0.110492978
## Lag 2e+05 0.031538927 0.059777863 0.031422409
## Lag 3e+05 -0.007739361 0.021279223 0.010830128
## Lag 4e+05 -0.009338163 0.008096436 -0.002092643
## Lag 5e+05 0.017111757 0.018333974 0.010704910
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.14241717 0.25304256 0.31374017
## Lag 2e+05 0.05253565 0.15484774 0.15621465
## Lag 3e+05 0.02646135 0.10982087 0.08059268
## Lag 4e+05 0.01227566 0.08029681 0.06826459
## Lag 5e+05 0.01705697 0.06588981 0.04118374
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000
## Lag 1e+05 0.074879985 0.377703664 0.6456391
## Lag 2e+05 0.021391225 0.177536210 0.4630338
## Lag 3e+05 0.002494948 0.093499035 0.3576147
## Lag 4e+05 -0.002622478 0.036140464 0.2886073
## Lag 5e+05 0.024470010 0.002873477 0.2378466
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.09375507 0.064630883
## Lag 2e+05 0.01997060 0.030699169
## Lag 3e+05 -0.00598792 0.007579963
## Lag 4e+05 -0.01336510 0.001474114
## Lag 5e+05 0.01421596 0.011660918
## Chain 2
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.071161082 0.09964516 0.111871853
## Lag 2e+05 0.014021123 0.03775006 0.028391814
## Lag 3e+05 0.018637877 0.01840297 0.018981688
## Lag 4e+05 0.013751366 0.01816882 -0.002557242
## Lag 5e+05 0.009689685 0.02606483 0.017403474
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.14478171 0.27695034 0.31408893
## Lag 2e+05 0.05972074 0.15570468 0.14129951
## Lag 3e+05 0.03956146 0.11768257 0.09576589
## Lag 4e+05 0.04866180 0.07457320 0.05878552
## Lag 5e+05 0.02093870 0.06356941 0.03161948
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 1.00000000 1.0000000
## Lag 1e+05 0.04510353 0.38989610 0.6583687
## Lag 2e+05 0.01386166 0.21368255 0.4708608
## Lag 3e+05 0.01226925 0.13246351 0.3622721
## Lag 4e+05 0.01861134 0.08104427 0.2784181
## Lag 5e+05 0.02171884 0.04615464 0.2082534
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 0.086155215 0.03925754
## Lag 2e+05 0.026923235 0.02601246
## Lag 3e+05 0.017841721 0.01582519
## Lag 4e+05 0.002769858 0.02232510
## Lag 5e+05 0.008264455 0.02023145
## Chain 3
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.07582691 0.093990020 0.085117896
## Lag 2e+05 0.01989535 0.017074329 0.047334780
## Lag 3e+05 0.01043223 0.004288192 0.024277235
## Lag 4e+05 0.02051186 0.017895157 0.009201237
## Lag 5e+05 0.01790323 0.034018615 0.016581692
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.0000000000 1.00000000 1.00000000
## Lag 1e+05 0.1377170449 0.28154018 0.29995351
## Lag 2e+05 0.0664960834 0.16184328 0.15677327
## Lag 3e+05 0.0258817417 0.11943528 0.07704985
## Lag 4e+05 0.0132465470 0.09038241 0.06400598
## Lag 5e+05 -0.0009876066 0.09090222 0.03406812
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.0000000
## Lag 1e+05 0.070709564 0.37579463 0.6685440
## Lag 2e+05 0.018847162 0.18953690 0.4888037
## Lag 3e+05 0.010422872 0.11338989 0.3775255
## Lag 4e+05 0.003262035 0.06584261 0.2978463
## Lag 5e+05 -0.012478781 0.03233593 0.2416851
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 0.087165904 0.05819432
## Lag 2e+05 0.026612748 0.03449705
## Lag 3e+05 0.005028087 0.00911260
## Lag 4e+05 0.008466851 0.01282287
## Lag 5e+05 0.002497070 0.01719685
## Chain 4
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.07455723 0.107652003 0.119948741
## Lag 2e+05 0.02348629 0.035077032 0.031700545
## Lag 3e+05 0.01353666 0.018762814 0.053084163
## Lag 4e+05 -0.01815397 0.007800711 0.003256013
## Lag 5e+05 0.01363323 0.017052104 0.020491764
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.148301985 0.27301246 0.31995172
## Lag 2e+05 0.068845800 0.15259366 0.15655315
## Lag 3e+05 0.051121302 0.10218236 0.08284653
## Lag 4e+05 0.025988934 0.06492904 0.05883851
## Lag 5e+05 0.007068315 0.07338353 0.03982063
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.0000000
## Lag 1e+05 0.063239068 0.38732048 0.6587763
## Lag 2e+05 -0.007413423 0.18552828 0.4784102
## Lag 3e+05 0.007604037 0.11091485 0.3682465
## Lag 4e+05 0.003198498 0.06643347 0.2919841
## Lag 5e+05 0.017640815 0.03707415 0.2264646
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 0.088038209 0.06219512
## Lag 2e+05 0.033544315 0.03505381
## Lag 3e+05 0.021391314 0.02123060
## Lag 4e+05 -0.001627703 0.01036265
## Lag 5e+05 0.013334294 0.03339782
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.51107 -0.35024 0.43544
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 1.31673 0.13636 0.02073
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -1.48871 1.02459 0.99469
## nodematch.race..wa.O absdiff.sqrt.age
## 0.81663 0.95312
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.6092990 0.7261602 0.6632400
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.1879296 0.8915381 0.9834575
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.1365637 0.3055578 0.3198887
## nodematch.race..wa.O absdiff.sqrt.age
## 0.4141416 0.3405268
## Joint P-value (lower = worse): 0.3338542 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.21918 1.12707 0.13326
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.62608 0.30627 1.71565
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -1.47700 -1.39737 -0.08671
## nodematch.race..wa.O absdiff.sqrt.age
## -0.16735 -0.65102
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.82651064 0.25971258 0.89398458
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.53126184 0.75939821 0.08622655
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.13967551 0.16230084 0.93090335
## nodematch.race..wa.O absdiff.sqrt.age
## 0.86709243 0.51503409
## Joint P-value (lower = worse): 0.3880088 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 1.0115 1.5089 1.6872
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.3884 2.6835 -0.1152
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 1.0640 0.3469 1.7141
## nodematch.race..wa.O absdiff.sqrt.age
## -0.0508 -1.0169
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.311791736 0.131332993 0.091563334
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.697731106 0.007284985 0.908262943
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.287306810 0.728702087 0.086518133
## nodematch.race..wa.O absdiff.sqrt.age
## 0.959483440 0.309191324
## Joint P-value (lower = worse): 0.1309634 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.53001 0.56466 2.96096
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.74092 -1.57454 0.44129
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -0.04032 0.23899 -0.75595
## nodematch.race..wa.O absdiff.sqrt.age
## 0.50621 -1.86642
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.596101908 0.572307510 0.003066782
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.458741289 0.115361709 0.659002271
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.967840994 0.811113852 0.449678470
## nodematch.race..wa.O absdiff.sqrt.age
## 0.612708512 0.061981929
## Joint P-value (lower = worse): 0.00489535 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Summary of model fit
Model 1
summary(est.m.testregionmix.bal[[1]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.pers") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + absdiff("sqrt.age") + nodematch("region",
## diff = FALSE) + degrange(from = 2) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x55c0a3cb7d38>
##
## Iterations: 78 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -9.25964 0.19863 0 < 1e-04 ***
## nodefactor.deg.pers.1 -0.31045 0.06342 0 < 1e-04 ***
## nodefactor.deg.pers.2 -0.08136 0.05998 0 0.17495
## nodefactor.race..wa.B 0.56265 0.16816 0 0.00082 ***
## nodefactor.race..wa.H 1.42535 0.18488 0 < 1e-04 ***
## nodefactor.region.EW 0.07111 0.07069 0 0.31441
## nodefactor.region.OW -0.23532 0.04286 0 < 1e-04 ***
## nodematch.race..wa.B 1.31832 0.25937 0 < 1e-04 ***
## nodematch.race..wa.H 0.63875 0.20789 0 0.00212 **
## nodematch.race..wa.O 1.57313 0.18844 0 < 1e-04 ***
## absdiff.sqrt.age -1.40262 0.04180 0 < 1e-04 ***
## nodematch.region 0.68246 0.04778 0 < 1e-04 ***
## deg2+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Model 2
summary(est.m.testregionmix.bal[[2]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.pers") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + absdiff("sqrt.age") + nodematch("region",
## diff = FALSE) + degrange(from = 2) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x55c0bb498a50>
##
## Iterations: 93 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -9.69593 0.19899 0 < 1e-04 ***
## nodefactor.deg.pers.1 -0.31012 0.06294 0 < 1e-04 ***
## nodefactor.deg.pers.2 -0.08094 0.05984 0 0.176179
## nodefactor.race..wa.B 0.57295 0.16871 0 0.000683 ***
## nodefactor.race..wa.H 1.43738 0.18380 0 < 1e-04 ***
## nodefactor.region.EW 0.26632 0.06711 0 < 1e-04 ***
## nodefactor.region.OW -0.15561 0.04128 0 0.000163 ***
## nodematch.race..wa.B 1.29726 0.25892 0 < 1e-04 ***
## nodematch.race..wa.H 0.61221 0.20698 0 0.003098 **
## nodematch.race..wa.O 1.58367 0.18823 0 < 1e-04 ***
## absdiff.sqrt.age -1.40149 0.04172 0 < 1e-04 ***
## nodematch.region 1.20624 0.04964 0 < 1e-04 ***
## deg2+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Model 3
summary(est.m.testregionmix.bal[[3]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.pers") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + absdiff("sqrt.age") + nodematch("region",
## diff = FALSE) + degrange(from = 2) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x55c0d03ba798>
##
## Iterations: 94 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -10.22432 0.20118 0 < 1e-04 ***
## nodefactor.deg.pers.1 -0.30978 0.06311 0 < 1e-04 ***
## nodefactor.deg.pers.2 -0.08073 0.05997 0 0.178207
## nodefactor.race..wa.B 0.58448 0.16856 0 0.000525 ***
## nodefactor.race..wa.H 1.45166 0.18485 0 < 1e-04 ***
## nodefactor.region.EW 0.42621 0.06433 0 < 1e-04 ***
## nodefactor.region.OW -0.09920 0.03974 0 0.012554 *
## nodematch.race..wa.B 1.27478 0.26058 0 < 1e-04 ***
## nodematch.race..wa.H 0.58276 0.20828 0 0.005143 **
## nodematch.race..wa.O 1.59706 0.18898 0 < 1e-04 ***
## absdiff.sqrt.age -1.40241 0.04182 0 < 1e-04 ***
## nodematch.region 1.80745 0.05459 0 < 1e-04 ***
## deg2+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Model 4
summary(est.m.testregionmix.bal[[4]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.pers") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + absdiff("sqrt.age") + nodematch("region",
## diff = FALSE) + degrange(from = 2) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x55c0e52e3410>
##
## Iterations: 78 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -10.22489 0.20101 0 < 1e-04 ***
## nodefactor.deg.pers.1 -0.31042 0.06292 0 < 1e-04 ***
## nodefactor.deg.pers.2 -0.08150 0.05983 0 0.173179
## nodefactor.race..wa.B 0.58557 0.16772 0 0.000481 ***
## nodefactor.race..wa.H 1.45353 0.18423 0 < 1e-04 ***
## nodefactor.region.EW 0.42589 0.06477 0 < 1e-04 ***
## nodefactor.region.OW -0.09899 0.03966 0 0.012557 *
## nodematch.race..wa.B 1.27712 0.25808 0 < 1e-04 ***
## nodematch.race..wa.H 0.57965 0.20837 0 0.005406 **
## nodematch.race..wa.O 1.59804 0.18842 0 < 1e-04 ***
## absdiff.sqrt.age -1.40234 0.04169 0 < 1e-04 ***
## nodematch.region 1.80723 0.05521 0 < 1e-04 ***
## deg2+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Model 5
summary(est.m.testregionmix.bal[[5]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.pers") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + absdiff("sqrt.age") + degrange(from = 2) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2)) +
## offset(nodemix("region", base = c(1, 3, 6)))
## <environment: 0x55c0fa2a04a0>
##
## Iterations: 68 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -8.29935 0.19422 0 < 1e-04 ***
## nodefactor.deg.pers.1 -0.31071 0.06316 0 < 1e-04 ***
## nodefactor.deg.pers.2 -0.08107 0.06014 0 0.177668
## nodefactor.race..wa.B 0.60974 0.16806 0 0.000286 ***
## nodefactor.race..wa.H 1.48185 0.18411 0 < 1e-04 ***
## nodefactor.region.EW 0.66064 0.06010 0 < 1e-04 ***
## nodefactor.region.OW -0.02416 0.03784 0 0.523215
## nodematch.race..wa.B 1.22445 0.25913 0 < 1e-04 ***
## nodematch.race..wa.H 0.52191 0.20906 0 0.012543 *
## nodematch.race..wa.O 1.62344 0.18779 0 < 1e-04 ***
## absdiff.sqrt.age -1.40236 0.04196 0 < 1e-04 ***
## deg2+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## mix.region.EW.KC -Inf 0.00000 0 < 1e-04 ***
## mix.region.EW.OW -Inf 0.00000 0 < 1e-04 ***
## mix.region.KC.OW -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R mix.region.EW.KC mix.region.EW.OW mix.region.KC.OW
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Network diagnostics
Model 1
(dx_main1 <- netdx(est.m.testregionmix.bal[[1]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.testregionmix.bal[[1]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+5, MCMC.burnin.max = 1e+5)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.500 2235.613 -0.002 28.633
## nodefactor.deg.pers.1 493.000 495.860 0.006 16.495
## nodefactor.deg.pers.2 603.000 597.120 -0.010 20.154
## nodefactor.race..wa.B 213.834 213.206 -0.003 12.101
## nodefactor.race..wa.H 587.844 584.291 -0.006 16.694
## nodefactor.region.EW 445.561 445.055 -0.001 18.149
## nodefactor.region.OW 1278.131 1274.015 -0.003 30.358
## nodematch.race..wa.B 31.177 32.195 0.033 5.775
## nodematch.race..wa.H 123.300 120.953 -0.019 8.141
## nodematch.race..wa.O 1638.946 1636.748 -0.001 25.609
## absdiff.sqrt.age 1206.285 1205.940 0.000 25.550
## nodematch.region 1344.300 1339.663 -0.003 26.906
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 125.638 -0.179 119.964
## Pct Edges Diss 0.007 0.007 0.004 0.002plot(dx_main1, type="formation")
plot(dx_main1, type="duration")
plot(dx_main1, type="dissolution")
Model 2
(dx_main2 <- netdx(est.m.testregionmix.bal[[2]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.testregionmix.bal[[1]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+5, MCMC.burnin.max = 1e+5)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.500 2236.960 -0.002 27.881
## nodefactor.deg.pers.1 493.000 492.382 -0.001 15.419
## nodefactor.deg.pers.2 603.000 598.317 -0.008 17.498
## nodefactor.race..wa.B 213.834 212.844 -0.005 12.631
## nodefactor.race..wa.H 587.844 591.506 0.006 18.530
## nodefactor.region.EW 445.561 448.103 0.006 17.478
## nodefactor.region.OW 1278.131 1278.594 0.000 32.563
## nodematch.race..wa.B 31.177 31.523 0.011 5.081
## nodematch.race..wa.H 123.300 125.105 0.015 8.300
## nodematch.race..wa.O 1638.946 1634.041 -0.003 23.335
## absdiff.sqrt.age 1206.285 1211.555 0.004 26.112
## nodematch.region 1568.350 1565.861 -0.002 25.771
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 125.982 -0.177 120.454
## Pct Edges Diss 0.007 0.007 0.001 0.002plot(dx_main2, type="formation")
plot(dx_main2, type="duration")
plot(dx_main2, type="dissolution")
Model 3
(dx_main3 <- netdx(est.m.testregionmix.bal[[3]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.testregionmix.bal[[1]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+5, MCMC.burnin.max = 1e+5)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.500 2236.675 -0.002 28.961
## nodefactor.deg.pers.1 493.000 492.246 -0.002 17.882
## nodefactor.deg.pers.2 603.000 600.378 -0.004 18.388
## nodefactor.race..wa.B 213.834 212.566 -0.006 11.853
## nodefactor.race..wa.H 587.844 588.229 0.001 16.020
## nodefactor.region.EW 445.561 444.745 -0.002 20.167
## nodefactor.region.OW 1278.131 1276.117 -0.002 33.012
## nodematch.race..wa.B 31.177 31.819 0.021 5.226
## nodematch.race..wa.H 123.300 124.916 0.013 8.248
## nodematch.race..wa.O 1638.946 1638.238 0.000 26.312
## absdiff.sqrt.age 1206.285 1204.905 -0.001 27.461
## nodematch.region 1792.400 1794.684 0.001 30.480
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 125.629 -0.179 120.441
## Pct Edges Diss 0.007 0.007 0.001 0.002plot(dx_main3, type="formation")
plot(dx_main3, type="duration")
plot(dx_main3, type="dissolution")
Model 4
(dx_main4 <- netdx(est.m.testregionmix.bal[[4]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.testregionmix.bal[[1]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+5, MCMC.burnin.max = 1e+5)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.500 2234.763 -0.003 28.814
## nodefactor.deg.pers.1 493.000 490.301 -0.005 17.620
## nodefactor.deg.pers.2 603.000 599.507 -0.006 18.690
## nodefactor.race..wa.B 213.834 212.411 -0.007 12.672
## nodefactor.race..wa.H 587.844 584.860 -0.005 18.157
## nodefactor.region.EW 445.561 441.840 -0.008 17.350
## nodefactor.region.OW 1278.131 1276.375 -0.001 32.163
## nodematch.race..wa.B 31.177 31.585 0.013 4.704
## nodematch.race..wa.H 123.300 120.442 -0.023 8.291
## nodematch.race..wa.O 1638.946 1634.244 -0.003 25.926
## absdiff.sqrt.age 1206.285 1203.876 -0.002 28.488
## nodematch.region 1792.400 1786.379 -0.003 27.351
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 125.777 -0.178 120.407
## Pct Edges Diss 0.007 0.007 0.001 0.002plot(dx_main4, type="formation")
plot(dx_main4, type="duration")
plot(dx_main4, type="dissolution")
Model 5
(dx_main5 <- netdx(est.m.testregionmix.bal[[5]], nsims = 10, nsteps = 1000, ncores = 4, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+5, MCMC.burnin.max = 1e+5)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.500 2233.495 -0.003 28.898
## nodefactor.deg.pers.1 493.000 489.539 -0.007 18.254
## nodefactor.deg.pers.2 603.000 599.975 -0.005 19.861
## nodefactor.race..wa.B 213.834 212.744 -0.005 12.073
## nodefactor.race..wa.H 587.844 585.580 -0.004 16.645
## nodefactor.region.EW 445.561 447.412 0.004 17.053
## nodefactor.region.OW 1278.131 1271.397 -0.005 31.082
## nodematch.race..wa.B 31.177 30.901 -0.009 4.753
## nodematch.race..wa.H 123.300 121.535 -0.014 8.655
## nodematch.race..wa.O 1638.946 1633.932 -0.003 24.802
## absdiff.sqrt.age 1206.285 1211.207 0.004 27.842
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
## mix.region.EW.KC NA 0.000 NA 0.000
## mix.region.EW.OW NA 0.000 NA 0.000
## mix.region.KC.OW NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 125.464 -0.180 120.163
## Pct Edges Diss 0.007 0.007 -0.001 0.002plot(dx_main5, type="formation")
plot(dx_main5, type="duration")
plot(dx_main5, type="dissolution")